Research Article | Aspiring Minds

How data science and predictive analytics can enable better hiring mechanisms for enterprises

The cost of a bad hire an organisation bears is beyond monetary – time spent in recruiting/training, loss of productivity, impaired employee morale are some of the aftermaths of a bad decision. At present, the HR function is adversely affected by the lack of structured information which if analysed can provide key insights into the system. While effective recruitment is at the core of their responsibilities and may seem intrinsic, hiring the right candidate for the right job in a cost and time effective manner is one of the key challenges faced by modern day HR. To add to this, more than a million candidates are entering the workforce every year thus increasing the process of talent filtering.

All recruiters typically receive a resume which has a lot of information but no clear mention of skills or competencies. The candidates are further subject to scrutiny by couple of line managers who may or may not be trained for interviewing and may form an opinion based solely on their interaction. In this entire process the decision making becomes quite subjective. The question is how can we bring objectivity to this process?

With the growing amount of data present around us, data science and predictive analytics can revolutionise hiring mechanisms to make hiring more objective and democratic. The problem statement here is simple – There is supply (pool of job seekers exists) and demand (pool of jobs exist) but there is no match-making of job seekers with the right jobs. The answer lies in inculcating a conscious movement towards a culture of data science. Data science in simple terms is an inference science which helps us make objective decisions and also allows us to know how effective those decisions would be.

Many organisations have just begun to invest in analytics but with the benefits associated with data driven decisions, the time is not far when data science will seep into every aspect of recruitment. To streamline hiring, an organisation can extract data for all the employees it hired in the previous year and quantify basis parameters like educational qualification, experience, test scores, skills and more to predict which of them have been successful and which of them have not. The insights collected from such analytics will allow organisations to move beyond subjective hiring and recruit the right talent from a more scientifically shortlisted pool. In a recent case, Xerox was able to reduce the attrition in its call centres by using algorithm driven recruitment techniques. The insights showed that employees without any call center experience were just as successful as those that had experience and that creative people were more likely to stay longer. This allowed the company to widen its hiring pool and improve the quality of hires and was able to cut down on attrition by 20%.

Gathering the vast amount of data can help recruiters identify the right talent by classifying information into trends and narrow down the talent pool. This will also help save cost, time and resources that would have been otherwise spent in the recruitment process. However, many organizations still rely on conventional hiring methods which are largely un-scalable and thus leading to them lose out on the right talent. Enterprises can significantly improve output and efficiency in hiring with data science enabling them to hire the right talent with the least effort with the help of numbers.

Data science coupled with objective scores becomes a very powerful tool for companies to determine the right recruitment standards. In a recent case, A Fortune 500 company wanted to establish hiring criteria based on objective measures. They worked with an assessment partner to conduct job analysis to understand the Knowledge, Skills, Ability and other prerequisites for the profile. Based on the analysis, they hired through a set of skill based pre-employment tests. In the next year, the company noticed that in new hires, the percentage of high performers had increased to 39% from 23% thereby leading to a 70% increase in high performers and 65% reduction in low performers. 

Recruitment is not the only aspect where HRs can benefit from data analytics but it can also help enterprises deal better with retention, once a candidate is on the job. Usually, a higher salary package is offered to retain an employee which may only be a short-term solution. According to a Deloitte research, companies that are incorporating analytics in HR have two to three times better results in the quality of hire, leadership development and employee turnover. Studying resignation patterns, common features of exiting employees, job satisfaction levels among other information can lead to insights which will HRs them adopt the right approach with employees and cut down on the attrition rate which is critical in today’s increasingly competitive landscape. Predictive analytics can help employers understand the workforce, the way marketing teams analyse data to understand and predict customer behaviour and patterns. These issues were earlier unquantifiable but modern data science methods are changing the way organisations can benefit from this data.

Human Resource is a relatively new domain which is seeing this technological invasion but structuring this vast data can benefit not just organisations but also the employment ecosystem on the whole. Investing in resources and knowledge should be a priority for enterprises as the value-driven insights will help address real business problems. It is essential that we leverage the predictive power of data science to bring in structured information and enable building of effective recruitment mechanisms for the future.

The article originally appeared in Dataquest

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